Interactive models increasingly hand complex reasoning and tool use to a slow brain in the background, leaving a realtime front model free to sustain full-duplex dialogue. Yet the user may refine, cancel or replace a request while its task runs, so a plan or result that made sense a moment ago goes stale. Each new utte...
Yu-Xiang Wang, Sheng-Bo Cai, Zhi-Xian Zhao et al.· 0 citations
Affine Fields for Interface Repair across Modalities (AFFIRM) is introduced, which first learns and freezes a base correction for speech hidden states, then adds query-specific corrections with zero mean over the operator-training queries.
Ming-Hao Hsu, Yu-Xiang Wang, Ying Shen et al.· 1 citation
Generative speech enhancement models can produce cleaner and more natural-sounding speech than conventional discriminative approaches, but may hallucinate by changing speech content or speaker identity, even with transcript conditioning. We present AuraSE, a flow-matching framework that addresses hallucination through...
Ying Shen, Yao Qian, Yuxuan Hu et al.· 0 citations
Voice agents are converging on a collaboration pattern: a full-duplex interaction model stays on the live channel as the entry to the conversation, while search, reasoning, and coding are handled through asynchronous delegation. A duplex model supports continuous listening and speaking, but complex reasoning and tool u...
Ying Shen, Yu-Xiang Wang, Kun-Yu Feng et al.· 0 citations
Model intelligence and fast response jointly shape the quality of interaction with speech language models, yet remain difficult to achieve together. Explicit chain-of-thought (CoT) improves reasoning and audio understanding, but generating intermediate reasoning tokens delays responses. Describing fine-grained acoustic...
Yu-Xiang Wang, Kun-Yu Feng, Yuan-Cheng Wang et al.· 2 citations
This work combines boundary interpretation and controlled reconstruction analysis by comparing predicted boundaries with linguistic and acoustic references and finds that boundary meaning depends on the underlying speech representation.
Han Wang, Jia-Qi Li, Ying Shen et al.· 0 citations
LoopSLM builds on looped Transformers for latent reasoning, reusing a decoder block to refine hidden states with acoustic grounding at every pass, and further narrows the perception-reasoning gap by separating learning to reason from learning to respond, enabling direct inference without CoT.
Sheng-Bo Cai, Yu-Xiang Wang, Jing-Ran Xie et al.· 2 citations
EvoAudio is proposed, a recursive self-improvement system for audio understanding that is the first to evolve the model, waveforms, questions, and difficulty in one closed loop.
RecurTrace introduces Loop Memory Attention, which lets each looped layer attend to its own states from previous iterations along the loop-time axis, so the model can revisit earlier computations instead of relying on the latest state alone.
Yu-Xiang Wang, Kun-Yu Feng, Ying-Da Shen et al.· 7 citations
A knowledge-gated task-construction protocol is introduced that separates a task instruction from a compact artefact containing private conventions, reference tables, and utility operators, and it is shown that the retained tasks improve post-training.
Han-Lin Tian, Min-Hao Li, Yuhan Mi et al.· 0 citations
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